[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5077":3,"related-5077":127},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":95,"slug":98,"view_count":99,"doi":100,"paper":101,"created_at":126},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。",null,"Sensors","2026-10-15T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","智慧农业","农业物联网","智能灌溉","网络安全",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,65,72,78,84,90],{"code":33,"score":52,"supported":53,"title":54,"abstract":55,"tags":57},15,true,[34],[56,34],"灌溉",[30],{"code":40,"score":13,"supported":53,"title":59,"abstract":60,"tags":64},[],[61,62,63],"物联网","internet of things","IoT",[29],{"code":66,"score":67,"supported":53,"title":68,"abstract":69,"tags":71},"B7",3,[],[70],"greenhouse",[],{"code":73,"score":67,"supported":53,"title":74,"abstract":75,"tags":77},"C",[],[76],"livestock",[],{"code":79,"score":67,"supported":53,"title":80,"abstract":81,"tags":83},"F3",[],[82],"fertigation",[],{"code":85,"score":86,"supported":14,"title":87,"abstract":88,"tags":89},"I6",2,[],[],[27,31],{"code":91,"score":22,"supported":14,"title":92,"abstract":93,"tags":94},"I1",[],[],[28],[96,97],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077",0,"10.3390\u002Fs26206545",{"doi":100,"openalex_id":102,"authors":103,"venue":10,"cited_by_count":99,"oa_url":6,"card":119,"direction":123,"ingested_from":125},"W7222165876",[104,107,110,113,116],{"name":105,"orcid":106},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":108,"orcid":109},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":111,"orcid":112},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":114,"orcid":115},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":117,"orcid":118},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":120,"method":121,"finding":122,"direction":123,"opportunity":124},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","智慧农业 \u002F 农业物联网","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","openalex","2026-10-16T23:30:08.098924Z",{"total":128,"page":22,"page_size":128,"items":129},6,[130,207,286,391,453,520],{"id":131,"title":132,"url":133,"summary":134,"summary_zh":9,"content":9,"source_name":135,"source_url":133,"published_at":136,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":137,"score_detail":138,"sources":143,"tags":145,"primary_topic":148,"secondary_topics":149,"classification":151,"search_phrases":187,"slug":190,"view_count":67,"doi":191,"paper":192,"created_at":206},1258,"Integration of IoT and Sensor Technologies for Sustainable Smart Irrigation Systems: Trends, Challenges, and Future Directions","https:\u002F\u002Fdoi.org\u002F10.31272\u002Fajece.35","Efficient water management is a persistent challenge in modern agriculture, especially in arid and semi-arid regions. The development and adoption of advanced soil sensor technologies are essential for optimising water use and supporting sustainable, high-yield agricultural systems. This systematic review compares primary soil sensor methodologies and synthesises findings from 156 peer-reviewed studies published between 2015 and 2025. The review assesses dielectric techniques, including time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR), as well as capacitive sensors, tensiometers, neutron probes, and instruments for measuring salinity and pH. Meta-analytical results show that TDR systems achieve high accuracy (pooled RMSE: 0.016 m³\u002Fm³) but are associated with significant costs ($500–$1200 per unit). In contrast, low-cost capacitive sensors can provide acceptable accuracy (RMSE: 0.045 m³\u002Fm³) when rigorously calibrated. LoRaWAN (Long Range Wide Area Network) is identified as the most effective communication protocol for agricultural Internet of Things (IoT) applications, with transmission ranges of 2–15 km and battery lifespans of 2–10 years. The use of machine learning methods improves irrigation scheduling accuracy by 15–30%, and artificial intelligence (AI) systems can achieve water savings of 35–50%. Key research gaps include the absence of standardised calibration procedures, as 67% of studies lack comprehensive validation, limited long-term stability assessments, with only 23% reporting data beyond six months, and insufficient investigation of multi-sensor data fusion. The review concludes by identifying future research priorities to support the development of robust and scalable innovative irrigation systems for sustainable agriculture.","Academic Journal of Electrical and Computer Engineering","2026-08-30T00:00:00Z",81,{"impact":18,"substance":139,"depth":140,"authority":141,"freshness":86,"relevant":22,"comment":142},24,20,13,"系统综述整合156项研究，对比多种土壤传感器与通信协议，数据详实，对智慧灌溉技术发展有重要参考价值。",[144],{"name":135,"url":133},[28,29,146,30,147],"机器学习","传感器",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[150],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":152},{"candidates":153,"agricultural_evidence":53},[154,159,163,171,177,183],{"code":40,"score":155,"supported":53,"title":156,"abstract":157,"tags":158},16,[63],[62,63],[147,29],{"code":33,"score":17,"supported":53,"title":160,"abstract":161,"tags":162},[34],[34],[30],{"code":164,"score":165,"supported":53,"title":166,"abstract":167,"tags":170},"I2",7,[],[168,169],"machine learning","artificial intelligence",[146],{"code":172,"score":67,"supported":53,"title":173,"abstract":174,"tags":176},"H2",[],[175],"soil",[],{"code":178,"score":67,"supported":53,"title":179,"abstract":180,"tags":182},"H8",[],[181],"sustainable agriculture",[],{"code":91,"score":22,"supported":14,"title":184,"abstract":185,"tags":186},[],[],[28],[188,189],"农业物联网 智慧农业 智能灌溉 机器学习","农业物联网 智慧农业","农业物联网智慧农业智能灌溉机器学习-1258","10.31272\u002Fajece.35",{"doi":191,"openalex_id":193,"authors":194,"venue":135,"cited_by_count":99,"oa_url":133,"card":201,"direction":123,"ingested_from":125},"W7204712150",[195,197,199],{"name":196,"orcid":9},"Omar Talib Khazraji",{"name":198,"orcid":9},"Marwan J. Hussein",{"name":200,"orcid":9},"Ahmed M. Almawla",{"tldr":202,"method":203,"finding":204,"direction":123,"opportunity":205},"系统综述2015-2025年土壤传感器与IoT在智能灌溉中的应用，评估技术、成本与性能，指出研究空白","系统综述156篇研究，比较TDR、FDR、电容等传感器，结合LoRaWAN和机器","TDR精度高但贵，电容传感器性价比好；LoRaWAN最优；AI节水35-50%；缺乏标准化校准和长期","研究空白：多传感器数据融合、标准化校准协议、长期稳定性评估，可开发低成本高精度融合系统。","2026-09-01T04:03:11.465487Z",{"id":208,"title":209,"url":210,"summary":211,"summary_zh":212,"content":9,"source_name":213,"source_url":210,"published_at":214,"category":12,"cover_url":9,"hotness":215,"is_selected":14,"score":216,"score_detail":217,"sources":221,"tags":225,"primary_topic":229,"secondary_topics":230,"classification":232,"search_phrases":263,"slug":266,"view_count":99,"doi":267,"paper":268,"created_at":285},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","Zenodo (CERN European Organization for Nuclear Research)","2026-11-25T00:00:00Z",25,73,{"impact":18,"substance":218,"depth":219,"authority":141,"freshness":99,"relevant":22,"comment":220},21,17,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[222,223],{"name":213,"url":210},{"name":213,"url":224},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[28,226,29,227,228],"农业人工智能","区块链溯源","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[231],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":233},{"candidates":234,"agricultural_evidence":53},[235,239,243,248,254,259],{"code":33,"score":52,"supported":53,"title":236,"abstract":237,"tags":238},[34],[56,34],[228],{"code":40,"score":13,"supported":53,"title":240,"abstract":241,"tags":242},[],[61,62,63],[29],{"code":164,"score":244,"supported":53,"title":245,"abstract":246,"tags":247},4,[],[169],[226],{"code":249,"score":67,"supported":53,"title":250,"abstract":251,"tags":253},"G2",[],[252],"小农户",[],{"code":255,"score":22,"supported":14,"title":256,"abstract":257,"tags":258},"E4",[],[],[227],{"code":91,"score":22,"supported":14,"title":260,"abstract":261,"tags":262},[],[],[28],[264,265],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":267,"openalex_id":269,"authors":270,"venue":213,"cited_by_count":99,"oa_url":210,"card":279,"direction":123,"ingested_from":125},"W7223034914",[271,274,276],{"name":272,"orcid":273},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":275,"orcid":9},"Dr.T.Baskar",{"name":277,"orcid":278},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":280,"method":281,"finding":282,"direction":283,"opportunity":284},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":298,"tags":300,"primary_topic":304,"secondary_topics":305,"classification":312,"search_phrases":361,"slug":364,"view_count":99,"doi":365,"paper":366,"created_at":390},4989,"Advances in micro-irrigation and supplementary irrigation in millets of dryland ecosystems","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16312","Dryland ecosystems cover approximately 41 % of the Earth's terrestrial surface and support nearly two billion people. This review synthesises recent advances in micro-irrigation and supplementary irrigation strategies for enhancing millet productivity and resilience in dryland ecosystems. Drylands are characterised by low and erratic rainfall, high evapotranspiration, poor soil fertility and frequent drought stress, making agricultural production vulnerable to climate variability and water scarcity. Millets are drought-tolerant, climate-resilient cereals cultivated in dryland agroecosystems. Despite their resilience, millet productivity remains constrained by increasing rainfall variability, prolonged intra-seasonal dry spells and rising atmospheric aridity associated with climate change. Evidence from semi-arid and arid regions indicates that micro-irrigation systems, including drip, subsurface drip and micro-sprinkler irrigation, significantly improve yield and biomass accumulation compared to conventional surface irrigation methods. These irrigation systems also enhance water use efficiency (WUE) and nutrient use efficiency (NUE), making them highly suitable for millet production in dryland ecosystems. The integration of fertigation further improves nutrient synchronisation with crop demand, reduces leaching losses and supports soil fertility. The review also highlights optimised irrigation scheduling based on evapotranspiration, soil moisture thresholds, deficit irrigation principles and sensor-based automation. Smart irrigation technologies incorporating soil moisture sensors and Internet of Things (IoT)-based control systems show strong potential to enhance precision water management and reduce water consumption by up to 30–35 %. Their adoption, supported by technological innovation, policy frameworks and location-specific research, will promote sustainable water management and resource-use efficiency in millet-based dryland ecosystems. Future research should focus on optimising irrigation scheduling, smart technologies and cost-effective solutions for water-limited environments.","旱地生态系统约占地球陆地表面的41%，支撑着近20亿人口。本文综述了微灌与补充灌溉策略在提升旱地生态系统中小米生产力与恢复力方面的最新进展。旱地降水少且不稳定、蒸散量大、土壤肥力低、干旱胁迫频繁，使农业生产易受气候变率和缺水的影响。小米是种植于旱地农业生态系统中的耐旱、气候适应型谷物。尽管具有恢复力，小米生产力仍受降雨变率增大、季节性持续干旱期延长以及气候变化相关的大气干燥度上升的制约。来自半干旱和干旱地区的证据表明，与传统地面灌溉方式相比，微灌系统（包括滴灌、地下滴灌和微喷灌）显著提高了产量和生物量积累。这些灌溉系统还提高了水分利用效率（WUE）和养分利用效率（NUE），使其非常适合旱地生态系统中的小米生产。水肥一体化（fertigation）的整合进一步改善了养分与作物需求的同步性，减少了淋失损失，并有助于维持土壤肥力。本文还重点介绍了基于蒸散量、土壤水分阈值、亏缺灌溉原则和基于传感器的自动化的优化灌溉制度。结合土壤水分传感器和基于物联网（IoT）控制系统的智能灌溉技术，在提升精准水分管理和减少高达30%–35%的用水量方面展现出巨大潜力。在技术创新、政策框架和因地制宜研究的支持下，这些技术的采用将促进以小米为基础的旱地生态系统中的可持续水分管理和资源利用效率。未来研究应聚焦于优化灌溉制度、智能技术以及面向水资源受限环境的成本效益解决方案。","Plant Science Today","2026-10-14T00:00:00Z",76,{"impact":19,"substance":140,"depth":219,"authority":141,"freshness":296,"relevant":22,"comment":297},8,"系统综述微灌与补充灌溉提升旱地谷子产量与水肥效率，含物联网智能灌溉节水30-35%等实质结论，对旱作智慧农业有参考价值。",[299],{"name":292,"url":289},[28,29,301,302,303],"旱作农业","节水灌溉","谷子高粱",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[306,307],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":308,"slug":309,"name":310,"path":311,"parent_code":37},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":46,"locked":14,"version":47,"method":48,"evidence":313},{"candidates":314,"agricultural_evidence":53},[315,322,326,335,340,345,351,357],{"code":33,"score":316,"supported":53,"title":317,"abstract":318,"tags":321},19,[34],[56,319,34,320],"水资源","evapotranspiration",[301,302],{"code":40,"score":13,"supported":53,"title":323,"abstract":324,"tags":325},[],[61,62,63],[29],{"code":308,"score":21,"supported":53,"title":327,"abstract":328,"tags":334},[],[329,330,331,332,333],"气候变化","气候适应","干旱","climate change","drought",[],{"code":79,"score":128,"supported":53,"title":336,"abstract":337,"tags":339},[],[338,82],"水肥一体化",[],{"code":172,"score":128,"supported":53,"title":341,"abstract":342,"tags":344},[],[343,175],"土壤",[],{"code":346,"score":67,"supported":53,"title":347,"abstract":348,"tags":350},"A5",[],[349],"millet",[],{"code":352,"score":67,"supported":53,"title":353,"abstract":354,"tags":356},"H6",[],[355],"农业生态",[],{"code":91,"score":22,"supported":14,"title":358,"abstract":359,"tags":360},[],[],[28],[362,363],"微灌 补充灌溉 旱地 谷子","智慧灌溉 水分利用效率 谷子","微灌补充灌溉旱地谷子-4989","10.14719\u002Fpst.16312",{"doi":365,"openalex_id":367,"authors":368,"venue":292,"cited_by_count":99,"oa_url":384,"card":385,"direction":123,"ingested_from":125},"W7221796668",[369,372,375,378,381],{"name":370,"orcid":371},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":373,"orcid":374},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":376,"orcid":377},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":379,"orcid":380},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":382,"orcid":383},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":386,"method":387,"finding":388,"direction":123,"opportunity":389},"综述旱地微灌与补充灌溉提升小米产量与水分利用效率的研究进展。","综述滴灌、地下滴灌、微喷及水肥一体化、传感器与IoT智能灌溉技术。","微灌显著提高小米产量、WUE和NUE，智能灌溉可节水30–35%。","可研究低成本IoT灌溉决策模型在小米旱地的本地化适配与长期效应。","2026-10-15T23:30:08.971713Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":397,"source_url":394,"published_at":398,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":407,"secondary_topics":408,"classification":410,"search_phrases":429,"slug":432,"view_count":99,"doi":433,"paper":434,"created_at":452},4888,"A MATLAB-based AI-IoT simulation framework for LSTM-driven smart solar irrigation and renewable energy management","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74935-0","Smart irrigation offers a great avenue for better water use efficiency, and farming sustainability. However, most of the studies conducted so far tend to concentrate on the monitoring systems and platforms of the Internet of Things (IoT) or on the prediction models based on Artificial Intelligence (AI) (but not on both). To address this gap, this research study proposes an Artificial Intelligence (AI)-Internet of Things (IoT) system to predict the water consumption requirements and control the energy produced by a PV system along with a battery State of Charge (SOC) management system, Maximum Power Point Tracking (MPPT) algorithm, adaptive pump-speed control, and conceptual IoT communication architecture. A dataset of real environmental data from the NASA POWER database was used to test the framework for Egypt’s New Delta region. The LSTM model was able to classify the irrigation pump operating states with an accuracy of 96.11%, a recall of 0.9820, a precision of 0.9100, and an F1-score of 0.9446. It had a better performance than the two models, Artificial Neural Network (ANN) and Support Vector Machine (SVM) when compared and robustness testing with various independent training runs showed a stable performance in predicting. An estimated annual water-saving rate of 28.57% was obtained relative to the assumed conventional fixed-schedule baseline, while motor energy use was reduced by 74.74% through adaptive pump-speed control. A sensitivity analysis was also performed, showing that the model was stable to moderate variations in environmental and operating conditions. Combined, these findings suggest that the use of AI-based modelling for renewable energy management in the context of an irrigation simulation model built in MATLAB could be a valuable asset in improving irrigation efficiency and lowering water and energy use, which is a promising start for future implementation of hardware versions of intelligent, solar-powered irrigation devices.","智能灌溉为提高用水效率和农业可持续性提供了重要途径。然而，迄今为止大多数研究倾向于集中在物联网（IoT）的监测系统和平台，或基于人工智能（AI）的预测模型（但很少两者兼顾）。为弥补这一空白，本研究提出了一种人工智能（AI）-物联网（IoT）系统，用于预测用水需求并控制光伏系统产生的能量，同时包含电池荷电状态（SOC）管理系统、最大功率点跟踪（MPPT）算法、自适应泵速控制以及概念性物联网通信架构。研究采用NASA POWER数据库中的真实环境数据集，在埃及新三角洲地区对该框架进行了测试。LSTM模型能够对灌溉泵运行状态进行分类，准确率达到96.11%，召回率为0.9820，精确率为0.9100，F1分数为0.9446。与人工神经网络（ANN）和支持向量机（SVM）两个模型相比，该模型表现更优，且通过多次独立训练运行进行的鲁棒性测试表明其在预测方面具有稳定性能。相对于假设的传统固定调度基线，估计年节水率为28.57%，而通过自适应泵速控制，电机能耗降低了74.74%。研究还进行了敏感性分析，表明该模型对环境和运行条件的中等程度变化具有稳定性。综合来看，这些发现表明，在MATLAB中构建的灌溉仿真模型背景下，将基于AI的建模用于可再生能源管理，可能是提高灌溉效率、降低水和能源消耗的有价值手段，这为未来实现智能太阳能灌溉设备的硬件版本提供了一个有前景的开端。","Scientific Reports","2026-10-12T00:00:00Z",78,{"impact":155,"substance":18,"depth":19,"authority":20,"freshness":296,"relevant":22,"comment":401},"该研究提出AI-IoT融合的太阳能灌溉仿真框架，LSTM预测精度达96.11%，节水28.57%、节能74.74%，方法新颖且数据可靠，对智慧农业与农业能源管理有参考价值。",[403],{"name":397,"url":394},[28,226,30,405,406],"节水农业","光伏农业",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[409],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":411},{"candidates":412,"agricultural_evidence":53},[413,417,421,425],{"code":40,"score":219,"supported":53,"title":414,"abstract":415,"tags":416},[63],[61,62,63],[],{"code":33,"score":155,"supported":53,"title":418,"abstract":419,"tags":420},[34],[56,34],[30,405],{"code":164,"score":244,"supported":53,"title":422,"abstract":423,"tags":424},[],[169],[226],{"code":91,"score":22,"supported":14,"title":426,"abstract":427,"tags":428},[],[],[28],[430,431],"MATLAB AI-IoT 太阳能灌溉","LSTM 智能灌溉 埃及新三角洲","MATLABAI-IoT太阳能灌溉-4888","10.1038\u002Fs41598-026-74935-0",{"doi":433,"openalex_id":435,"authors":436,"venue":397,"cited_by_count":99,"oa_url":446,"card":447,"direction":123,"ingested_from":125},"W7221571268",[437,440,443],{"name":438,"orcid":439},"Ahmed Mashaly","https:\u002F\u002Forcid.org\u002F0009-0007-7009-4531",{"name":441,"orcid":442},"Ragab Abdelaziz El-Sehiemy","https:\u002F\u002Forcid.org\u002F0000-0002-3340-4031",{"name":444,"orcid":445},"Mohamed M. Elgohary","https:\u002F\u002Forcid.org\u002F0000-0003-4441-7812","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-74935-0.pdf",{"tldr":448,"method":449,"finding":450,"direction":123,"opportunity":451},"提出MATLAB中AI-IoT仿真框架，用LSTM预测灌溉需水并管理光伏与电池。","NASA POWER环境数据，LSTM对比ANN\u002FSVM，含MPPT与自适应泵速","LSTM分类准确率96.11%，节水28.57%，电机能耗降74.74%。","可延伸至真实硬件部署与多区域验证，并融合实时气象与土壤墒情数据。","2026-10-14T23:30:13.060384Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":9,"source_name":459,"source_url":456,"published_at":460,"category":12,"cover_url":9,"hotness":215,"is_selected":14,"score":461,"score_detail":462,"sources":464,"tags":468,"primary_topic":470,"secondary_topics":471,"classification":473,"search_phrases":498,"slug":501,"view_count":22,"doi":9,"paper":502,"created_at":519},4671,"A COMPREHENSIVE REVIEW ON IOT AND EDGE AI-BASED PRECISION AGRICULTURE SYSTEM FOR WATER OPTIMIZATION","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F316","Water scarcity and inefficient irrigation practices remain major challenges in modern agriculture, leading to reduced crop yield, resource wastage, and increased production costs. The proposed project, “IoT and Edge AI-Based Precision Agriculture System for Water Optimisation,” aims to develop an intelligent, real-time irrigation management system that optimises water usage through data-driven decision-making at the edge. The system integrates Internet of Things (IoT) sensors such as soil moisture, temperature, humidity, and light intensity sensors to continuously monitor field conditions. The collected data is processed locally using an Edge AI module, enabling real-time analysis and predictive irrigation control without relying entirely on cloud connectivity. By deploying machine learning algorithms at the edge, the system can determine optimal watering schedules based on soil conditions, crop requirements, and environmental factors, thereby minimising water wastage and ensuring efficient irrigation. A cloud platform is incorporated for long-term data storage, visualisation, and remote monitoring via a web or mobile dashboard. This hybrid architecture enhances scalability, reduces latency, and improves reliability in rural areas with limited internet connectivity. Automated irrigation control using smart valves or pumps ensures precise water delivery tailored to specific crop zones.","水资源短缺和灌溉效率低下仍是现代农业面临的主要挑战，导致作物减产、资源浪费和生产成本增加。本项目“基于物联网与边缘人工智能的水资源优化精准农业系统”旨在开发一种智能实时灌溉管理系统，通过边缘端的数据驱动决策来优化水资源利用。该系统集成了物联网（IoT）传感器，如土壤湿度、温度、湿度和光照强度传感器，以持续监测田间状况。采集的数据通过边缘人工智能模块在本地进行处理，实现实时分析和预测性灌溉控制，而无需完全依赖云连接。通过在边缘端部署机器学习算法，系统能够根据土壤条件、作物需求和环境因素确定最佳灌溉计划，从而最大限度地减少水资源浪费并确保高效灌溉。系统还包含一个云平台，用于长期数据存储、可视化以及通过网页或移动端仪表盘进行远程监控。这种混合架构增强了可扩展性，降低了延迟，并提高了互联网连接有限的农村地区的可靠性。利用智能阀门或水泵的自动灌溉控制可确保针对特定作物区域进行精准供水。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",53,{"impact":17,"substance":20,"depth":141,"authority":128,"freshness":296,"relevant":22,"comment":463},"综述性论文，系统梳理IoT与边缘AI在水肥优化中的应用，方法框架清晰但缺乏原创数据与实证，属细分领域参考性内容。",[465,466],{"name":459,"url":456},{"name":459,"url":467},"https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F315",[28,226,29,469,228],"边缘计算",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[472],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":474},{"candidates":475,"agricultural_evidence":53},[476,480,486,490,494],{"code":40,"score":316,"supported":53,"title":477,"abstract":478,"tags":479},[63],[61,62,63],[29,469],{"code":91,"score":52,"supported":53,"title":481,"abstract":483,"tags":485},[482],"precision agriculture",[484,482],"精准农业",[28],{"code":33,"score":13,"supported":53,"title":487,"abstract":488,"tags":489},[],[56,319,34],[228],{"code":164,"score":165,"supported":53,"title":491,"abstract":492,"tags":493},[],[146,168],[226],{"code":172,"score":128,"supported":53,"title":495,"abstract":496,"tags":497},[],[343,175],[],[499,500],"IoT 边缘AI 精准灌溉","智慧灌溉 土壤墒情 传感器","IoT边缘AI精准灌溉-4671",{"doi":9,"openalex_id":503,"authors":504,"venue":459,"cited_by_count":99,"oa_url":456,"card":514,"direction":123,"ingested_from":125},"W7221411136",[505,507,509,511],{"name":506,"orcid":9},"Om Gaikwad",{"name":508,"orcid":9},"Shriraj Chavan",{"name":510,"orcid":9},"Krushnadev Gaikwad",{"name":512,"orcid":513},"Swapnil R. Takale","https:\u002F\u002Forcid.org\u002F0009-0001-3636-4450",{"tldr":515,"method":516,"finding":517,"direction":123,"opportunity":518},"综述基于物联网与边缘AI的精准农业系统，实现实时灌溉优化与节水。","集成土壤湿度等IoT传感器与边缘AI，本地处理数据并云端存储可视化。","边缘AI可实时预测灌溉需求，减少水浪费并适应网络受限农村环境。","可探索边缘AI模型轻量化与多源数据融合，提升复杂农田环境下的灌溉决策精度。","2026-10-11T23:30:16.298334Z",{"id":521,"title":522,"url":523,"summary":524,"summary_zh":525,"content":9,"source_name":526,"source_url":523,"published_at":527,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":528,"score_detail":529,"sources":531,"tags":533,"primary_topic":536,"secondary_topics":537,"classification":539,"search_phrases":569,"slug":572,"view_count":86,"doi":573,"paper":574,"created_at":588},4561,"Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajese.20261004.11","This study presents a system architecture, algorithm, and evaluation protocol for smart agriculture and environmental monitoring. It is a method\u002Farchitecture contribution and does not report field trial results. World food production is still under stress from climate variability, the availability of freshwater resources, and the cost of farm inputs, offering an incentive to transition to data-driven and sensor-based farm management practices. Internet of Things (IoT)-based agricultural monitoring is well developed, but most systems deployed focus on a single parameter (e.g., soil moisture) or the parameter is inferred solely in the cloud, adding latency and fragility to the deployment of systems in rural areas. In this study, we propose an integrated architecture for low-power multiparameter environmental sensing, LoRaWAN\u002FNB-IoT connectivity, edge-based lightweight inference, and privacy-preserving federated learning with cross-farm model aggregation, which is further complemented by a digital twin layer for scenario simulation. The decision layer of the framework can combine data from ground sensors with UAV and satellite images for the multi-scale monitoring of crop health and environmental quality (air, water, and GHG proxies). We contribute the full system design, an algorithmic pipeline, a sensor and model configuration specification, and a pre-registered evaluation protocol intended to guide future field deployment; no field data have yet been collected and clearly labelled non-empirical templates showing the expected reporting format only, rather than measured outcomes. The architecture and protocol are instead grounded in, and cross-checked against, published state-of-the-art results, which report an accuracy range of 82–97% for federated crop-yield prediction and 20–30% water savings from sensor-controlled irrigation.","本研究提出了一套面向智慧农业与环境监测的系统架构、算法与评估协议。这是一项方法\u002F架构层面的贡献，不报告田间试验结果。世界粮食生产仍面临气候变率、淡水资源可得性以及农业投入成本带来的压力，这促使农业管理实践向数据驱动和基于传感器的方向转型。基于物联网（IoT）的农业监测已较为成熟，但大多数已部署系统仅关注单一参数（如土壤水分），或仅在云端推断参数，这增加了农村地区系统部署的延迟和脆弱性。在本研究中，我们提出了一种集成架构，涵盖低功耗多参数环境感知、LoRaWAN\u002FNB-IoT连接、基于边缘的轻量级推理，以及具有跨农场模型聚合的隐私保护联邦学习，并进一步辅以用于情景模拟的数字孪生层。该框架的决策层可将地面传感器数据与无人机和卫星图像相结合，用于作物健康和环境质量（空气、水和温室气体代理指标）的多尺度监测。我们贡献了完整的系统设计、算法流程、传感器与模型配置规范，以及一份预注册的评估协议，旨在指导未来的田间部署；目前尚未收集田间数据，并明确标注了非经验性模板，仅展示预期报告格式，而非实测结果。该架构和协议以已发表的最先进成果为依据并与之交叉验证，这些成果报告联邦作物产量预测的准确率范围为82–97%，传感器控制灌溉可节水20–30%。","American Journal of Environmental Science and Engineering","2026-10-09T00:00:00Z",66,{"impact":17,"substance":155,"depth":219,"authority":141,"freshness":296,"relevant":22,"comment":530},"提出边缘AI+IoT+联邦学习的抗气候风险农业监测架构与评估协议，方法设计完整但尚无田间实测数据，属有参考价值的预研性论文。",[532],{"name":526,"url":523},[28,29,469,534,535],"数字孪生","联邦学习",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[538],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":540},{"candidates":541,"agricultural_evidence":53},[542,548,552,556,560,565],{"code":91,"score":316,"supported":53,"title":543,"abstract":545,"tags":547},[544],"smart agriculture",[28,534,544,546],"digital twin",[534,28],{"code":40,"score":316,"supported":53,"title":549,"abstract":550,"tags":551},[63],[61,62,63],[29,469],{"code":33,"score":21,"supported":53,"title":553,"abstract":554,"tags":555},[],[56,319,34],[],{"code":172,"score":128,"supported":53,"title":557,"abstract":558,"tags":559},[],[343,175],[],{"code":66,"score":67,"supported":53,"title":561,"abstract":562,"tags":564},[],[563],"温室",[],{"code":164,"score":22,"supported":14,"title":566,"abstract":567,"tags":568},[],[],[535],[570,571],"LoRaWAN NB-IoT 农业监测","联邦学习 作物产量预测","LoRaWANNB-IoT农业监测-4561","10.11648\u002Fj.ajese.20261004.11",{"doi":573,"openalex_id":575,"authors":576,"venue":526,"cited_by_count":99,"oa_url":523,"card":583,"direction":123,"ingested_from":125},"W7221105407",[577,580],{"name":578,"orcid":579},"Zarin Progga","https:\u002F\u002Forcid.org\u002F0009-0004-2311-1210",{"name":581,"orcid":582},"Mohammad Ali","https:\u002F\u002Forcid.org\u002F0009-0009-3725-8535",{"tldr":584,"method":585,"finding":586,"direction":123,"opportunity":587},"提出边缘AI、物联网与联邦学习集成的智慧农业环境监测架构与评估协议，未开展实地试验。","LoRaWAN\u002FNB-IoT多参数传感、边缘轻量推理、跨农场联邦学习、数字孪生仿","架构基于已发表结果交叉验证，联邦产量预测精度82-97%，传感器灌溉节水20-30%。","该框架仅停留在设计阶段，未来可开展真实农田部署，验证边缘联邦学习在异构农场环境下的精度与通信开销。","2026-10-10T23:30:07.789940Z"]